ArticleCell reports methods2024
Tumor-associated antigen prediction using a single-sample gene expression state inference algorithm.
Article in Cell reports methods, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
11 citing papers in PubMed.
- Preclinical evaluation of SLC35D3-targeted CAR-T cells for tumor-selective immunotherapy.Biochemistry and biophysics reports · 2026Article
- Revisiting tumor immunogenicity through the lens of mutant p53: Implications for cancer immunotherapy.Apoptosis : an international journal on programmed cell death · 2026Review
- Emerging nanoimmunotherapeutic strategies for breast cancer.Discover oncology · 2026Review
- Immune interception in cancer: prioritizing vaccination in high-risk and premalignant settings.Frontiers in immunology · 2026Article
- PepQueryMHC: rapid and comprehensive tumor antigen prioritization from immunopeptidomics data.Genome biology · 2025Article
- An SNP-dependent cancer-testis antigenic epitope serves as a promising immunotherapeutic target for cancer.Oncoimmunology · 2025Article
- mRNA Cancer Vaccines: From Pandemic Paradigm to Personalized Oncology Therapeutics.Cancer innovation · 2025Review
- Article
- ZP4: A novel target for CAR-T cell therapy in triple negative breast cancer.Molecular therapy : the journal of the American Society of Gene Therapy · 2025Article
- Next-generation cancer vaccines: targeting cryptic and non-canonical antigens for precision immunotherapy.Exploration of targeted anti-tumor therapy · 2025Review
- Therapeutic Vaccination in Lung Cancer: Past Attempts, Current Approaches and Future Promises.Journal of respiratory biology and translational medicine · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
We developed a Bayesian-based algorithm to infer gene expression states in individual samples and incorporated it into a workflow to identify tumor-associated antigens (TAAs) across 33 cancer types using RNA sequencing (RNA-seq) data from the Genotype-Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA). Our analysis identified 212 candidate TAAs, with 78 validated in independent RNA-seq datasets spanning seven cancer types. Eighteen of these TAAs were further corroborated by proteomics data, including 10 linked to liver cancer. We predicted that 38 peptides derived from these 10 TAAs would bind strongly to HLA-A02, the most common HLA allele. Experimental validation confirmed significant binding affinity and immunogenicity for 21 of these peptides. Notably, approximately 64% of liver tumors expressed one or more TAAs associated with these 21 peptides, positioning them as promising candidates for liver cancer therapies, such as peptide vaccines or T cell receptor (TCR)-T cell treatments. This study highlights the power of integrating computational and experimental approaches to discover TAAs for immunotherapy.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.